\section{Conclusion}
\label{s:conclude}


Relation extraction has the potential to enable improved search and
question-answering applications by transforming information encoded
in natural language on the Web into structured form.  Unfortunately,
the most successful techniques for relation extraction are based on
supervised learning and require hundreds or thousands of training
examples; these are expensive and time-consuming to produce.  This
paper presents ontological smoothing, a novel method for learning
relational extractors, which requires only minimal supervision.  Our
approach is based on a new ontology-mapping algorithm, which uses
probabilistic joint inference over schema- and instance-based
features to search the space of views defined using SQL selection,
projection, join and union operators.  Experiments demonstrate the
method's promise, improving both precision and recall. Our \sys\
system learned significantly better extractors for 61 relations in
two target ontologies, by exploiting Freebase as a background
knowledge source.
